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Digital Farming, Food, and Environment—a Response to Farm Biosecurity and Food Safety Concerns

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: closed (10 January 2022) | Viewed by 18654

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Special Issue Editors

Dr. Iman Tahmasbian

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Guest Editor

Department of Agriculture and Fisheries, Queensland Government, Toowoomba, QLD 4350, Australia
Interests: hyperspectral imaging; computer vision; post-harvest quality control; remote sensing of environment; automated systems for monitoring animal farms; hyperspectral imaging applications in food systems throughout the food value chains; aquaculture; agriculture; enviroment; machine learning; chemometric; hyperspectral imaging for soil and plant quality measurments; image classification; non-destructive analysis; microscopic hyperspectral analysis

Dr. Kourosh Khoshelham

E-Mail Website
Guest Editor

Department of Infrastructure Engineering, The University of Melbourne, Melbourne, VIC 3010, Australia
Interests: photogrammetry; 3D computer vision; remote sensing; machine learning; deep learning; automated interpretation of imagery and point clouds
Special Issues, Collections and Topics in MDPI journals

Dr. Shahla Hosseini Bai

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Guest Editor

Centre for Planetary Health and Food Security, School of Environment and Science, Griffith University, Brisbane, QLD 4111, Australia
Interests: soil-plant interactions; hyperspectral imaging; Biochar; research for development; sustainable food production systems

Special Issue Information

Dear Colleagues,

The growing world population and increased demand for food have raised concerns about farm biosecurity and food safety. The need for digital monitoring and management systems that collect and analyse information from environmental sites, farms, animal sheds, food storage areas, and food processing lines in real-time has never been so evident. Digital quality control systems (DQCS) integrate remote and proximal sensor data with the application of machine learning and artificial intelligence algorithms in order to generate real-time information for a specific purpose. DQCS can be used to address biosecurity and safety concerns by reducing human–animal contact, identifying pathogens, detecting environmental pollutants, detecting food impurities and toxins, and differentiating between the preferred plant/animal species and undesirable or potentially dangerous species. This Special Issue aims to gather relevant research that uses digital systems to predict potential risks, help decision-makers to apply site-specific management practices, and decrease the human footprint in the environment.

Dr. Iman Tahmasbian
Dr. Kourosh Khoshelham
Dr. Shahla Hosseini Bai
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Biosecurity
Animal health
Food safety
Digital agriculture and aquaculture
Digital quality monitoring systems
Artificial intelligence
Hyperspectral imaging
Machine vision
Machine learning
Chemometrics
Image classification
Image processing
Hyperspectral sensors
Multispectral sensors
Environmental pollution
Near-infrared (NIR) spectroscopy

Published Papers (5 papers)

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Research

20 pages, 3577 KiB

Open AccessArticle

Time Series of Quad-Pol C-Band Synthetic Aperture Radar for the Forecasting of Crop Biophysical Variables of Barley Fields Using Statistical Techniques

by Ana E. Sipols, Rubén Valcarce-Diñeiro, Maria Teresa Santos-Martín, Nilda Sánchez and Clara Simón de Blas

Remote Sens. 2022, 14(3), 614; https://doi.org/10.3390/rs14030614 - 27 Jan 2022

Viewed by 2112

Abstract

This paper aims to both fit and predict crop biophysical variables with a SAR image series by performing a factorial experiment and estimating time series models using a combination of forecasts. Two plots of barley grown under rainfed conditions in Spain were monitored during the growing cycle of 2015 (February to June). The dataset included nine field estimations of agronomic parameters, 20 RADARSAT-2 images, and daily weather records. Ten polarimetric observables were retrieved and integrated to derive the six agronomic and monitoring variables, including the height, biomass, fraction of vegetation cover, leaf area index, water content, and soil moisture. The statistical methods applied, namely double smoothing, ARIMAX, and robust regression, allowed the adjustment and modelling of these field variables. The model equations showed a positive contribution of meteorological variables and a strong temporal component in the crop’s development, as occurs in natural conditions. After combining different models, the results showed the best efficiency in terms of forecasting and the influence of several weather variables. The existence of a cointegration relationship between the data series of the same crop in different fields allows for adjusting and predicting the results in other fields with similar crops without re-modelling. Full article

(This article belongs to the Special Issue Digital Farming, Food, and Environment—a Response to Farm Biosecurity and Food Safety Concerns)

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24 pages, 5658 KiB

Open AccessArticle

A Dual Attention Convolutional Neural Network for Crop Classification Using Time-Series Sentinel-2 Imagery

by Seyd Teymoor Seydi, Meisam Amani and Arsalan Ghorbanian

Remote Sens. 2022, 14(3), 498; https://doi.org/10.3390/rs14030498 - 21 Jan 2022

Cited by 31 | Viewed by 4220

Abstract

Accurate and timely mapping of crop types and having reliable information about the cultivation pattern/area play a key role in various applications, including food security and sustainable agriculture management. Remote sensing (RS) has extensively been employed for crop type classification. However, accurate mapping of crop types and extents is still a challenge, especially using traditional machine learning methods. Therefore, in this study, a novel framework based on a deep convolutional neural network (CNN) and a dual attention module (DAM) and using Sentinel-2 time-series datasets was proposed to classify crops. A new DAM was implemented to extract informative deep features by taking advantage of both spectral and spatial characteristics of Sentinel-2 datasets. The spectral and spatial attention modules (AMs) were respectively applied to investigate the behavior of crops during the growing season and their neighborhood properties (e.g., textural characteristics and spatial relation to surrounding crops). The proposed network contained two streams: (1) convolution blocks for deep feature extraction and (2) several DAMs, which were employed after each convolution block. The first stream included three multi-scale residual convolution blocks, where the spectral attention blocks were mainly applied to extract deep spectral features. The second stream was built using four multi-scale convolution blocks with a spatial AM. In this study, over 200,000 samples from six different crop types (i.e., alfalfa, broad bean, wheat, barley, canola, and garden) and three non-crop classes (i.e., built-up, barren, and water) were collected to train and validate the proposed framework. The results demonstrated that the proposed method achieved high overall accuracy and a Kappa coefficient of 98.54% and 0.981, respectively. It also outperformed other state-of-the-art classification methods, including RF, XGBOOST, R-CNN, 2D-CNN, 3D-CNN, and CBAM, indicating its high potential to discriminate different crop types. Full article

(This article belongs to the Special Issue Digital Farming, Food, and Environment—a Response to Farm Biosecurity and Food Safety Concerns)

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20 pages, 6709 KiB

Open AccessArticle

A Performance Evaluation of Vis/NIR Hyperspectral Imaging to Predict Curcumin Concentration in Fresh Turmeric Rhizomes

by Michael B. Farrar, Helen M. Wallace, Peter Brooks, Catherine M. Yule, Iman Tahmasbian, Peter K. Dunn and Shahla Hosseini Bai

Remote Sens. 2021, 13(9), 1807; https://doi.org/10.3390/rs13091807 - 06 May 2021

Cited by 9 | Viewed by 2729

Abstract

Hyperspectral image (HSI) analysis has the potential to estimate organic compounds in plants and foods. Curcumin is an important compound used to treat a range of medical conditions. Therefore, a method to rapidly determine rhizomes with high curcumin content on-farm would be of significant advantage for farmers. Curcumin content of rhizomes varies within, and between varieties but current chemical analysis methods are expensive and time consuming. This study compared curcumin in three turmeric (Curcuma longa) varieties and examined the potential for laboratory-based HSI to rapidly predict curcumin using the visible–near infrared (400–1000 nm) spectrum. Hyperspectral images (n = 152) of the fresh rhizome outer-skin and flesh were captured, using three local varieties (yellow, orange, and red). Distribution of curcuminoids and total curcumin was analysed. Partial least squares regression (PLSR) models were developed to predict total curcumin concentrations. Total curcumin and the proportion of three curcuminoids differed significantly among all varieties. Red turmeric had the highest total curcumin concentration (0.83 ± 0.21%) compared with orange (0.37 ± 0.12%) and yellow (0.02 ± 0.02%). PLSR models predicted curcumin using raw spectra of rhizome flesh and pooled data for all three varieties (R²_c = 0.83, R²_p = 0.55, ratio of prediction to deviation (RPD) = 1.51) and was slightly improved by using images of a single variety (orange) only (R²_c = 0.85, R²_p = 0.62, RPD = 1.65). However, prediction of curcumin using outer-skin of rhizomes was poor (R²_c = 0.64, R²_p = 0.37, RPD = 1.28). These models can discriminate between ‘low’ and ‘high’ values and so may be adapted into a two-level grading system. HSI has the potential to help identify turmeric rhizomes with high curcumin concentrations and allow for more efficient refinement into curcumin for medicinal purposes. Full article

(This article belongs to the Special Issue Digital Farming, Food, and Environment—a Response to Farm Biosecurity and Food Safety Concerns)

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15 pages, 2686 KiB

Open AccessArticle

Comparison of Hyperspectral Imaging and Near-Infrared Spectroscopy to Determine Nitrogen and Carbon Concentrations in Wheat

by Iman Tahmasbian, Natalie K. Morgan, Shahla Hosseini Bai, Mark W. Dunlop and Amy F. Moss

Remote Sens. 2021, 13(6), 1128; https://doi.org/10.3390/rs13061128 - 16 Mar 2021

Cited by 25 | Viewed by 4176

Abstract

Hyperspectral imaging (HSI) is an emerging rapid and non-destructive technology that has promising application within feed mills and processing plants in poultry and other intensive animal industries. HSI may be advantageous over near infrared spectroscopy (NIRS) as it scans entire samples, which enables compositional gradients and sample heterogenicity to be visualised and analysed. This study was a preliminary investigation to compare the performance of HSI with that of NIRS for quality measurements of ground samples of Australian wheat and to identify the most important spectral regions for predicting carbon (C) and nitrogen (N) concentrations. In total, 69 samples were scanned using an NIRS (400–2500 nm), and two HSI cameras operated in 400–1000 nm (VNIR) and 1000–2500 nm (SWIR) spectral regions. Partial least square regression (PLSR) models were used to correlate C and N concentrations of 63 calibration samples with their spectral reflectance, with 6 additional samples used for testing the models. The accuracy of the HSI predictions (full spectra) were similar or slightly higher than those of NIRS (NIRS

R_{c}^{2}

for C = 0.90 and N = 0.96 vs. HSI

R_{c}^{2}

for C (VNIR) = 0.97 and N (SWIR) = 0.97). The most important spectral region for C prediction identified using HSI reflectance was 400–550 nm with R² of 0.93 and RMSE of 0.17% in the calibration set and R² of 0.86, RMSE of 0.21% and ratio of performance to deviation (RPD) of 2.03 in the test set. The most important spectral regions for predicting N concentrations in the feed samples included 1451–1600 nm, 1901–2050 nm and 2051–2200 nm, providing prediction with R² ranging from 0.91 to 0.93, RMSE ranging from 0.06% to 0.07% in the calibration sets, R² from 0.96 to 0.99, RMSE of 0.06% and RPD from 3.47 to 3.92 in the test sets. The prediction accuracy of HSI and NIRS were comparable possibly due to the larger statistical population (larger number of pixels) that HSI provided, despite the fact that HSI had smaller spectral range compared with that of NIRS. In addition, HSI enabled visualising the variability of C and N in the samples. Therefore, HSI is advantageous compared to NIRS as it is a multifunctional tool that poses many potential applications in data collection and quality assurance within feed mills and poultry processing plants. The ability to more accurately measure and visualise the properties of feed ingredients has potential economic benefits and therefore additional investigation and development of HSI in this application is warranted. Full article

(This article belongs to the Special Issue Digital Farming, Food, and Environment—a Response to Farm Biosecurity and Food Safety Concerns)

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19 pages, 4443 KiB

Open AccessArticle

Rapid Determination of Nutrient Concentrations in Hass Avocado Fruit by Vis/NIR Hyperspectral Imaging of Flesh or Skin

by Wiebke Kämper, Stephen J. Trueman, Iman Tahmasbian and Shahla Hosseini Bai

Remote Sens. 2020, 12(20), 3409; https://doi.org/10.3390/rs12203409 - 17 Oct 2020

Cited by 28 | Viewed by 3969

Abstract

Fatty acid composition and mineral nutrient concentrations can affect the nutritional and postharvest properties of fruit and so assessing the chemistry of fresh produce is important for guaranteeing consistent quality throughout the value chain. Current laboratory methods for assessing fruit quality are time-consuming and often destructive. Non-destructive technologies are emerging that predict fruit quality and can minimise postharvest losses, but it may be difficult to develop such technologies for fruit with thick skin. This study aimed to develop laboratory-based hyperspectral imaging methods (400–1000 nm) for predicting proportions of six fatty acids, ratios of saturated and unsaturated fatty acids, and the concentrations of 14 mineral nutrients in Hass avocado fruit from 219 flesh and 194 skin images. Partial least squares regression (PLSR) models predicted the ratio of unsaturated to saturated fatty acids in avocado fruit from both flesh images (R² = 0.79, ratio of prediction to deviation (RPD) = 2.06) and skin images (R² = 0.62, RPD = 1.48). The best-fit models predicted parameters that affect postharvest processing such as the ratio of oleic:linoleic acid from flesh images (R² = 0.67, RPD = 1.63) and the concentrations of boron (B) and calcium (Ca) from flesh images (B: R² = 0.61, RPD = 1.51; Ca: R² = 0.53, RPD = 1.71) and skin images (B: R² = 0.60, RPD = 1.55; Ca: R² = 0.68, RPD = 1.57). Many quality parameters predicted from flesh images could also be predicted from skin images. Hyperspectral imaging represents a promising tool to reduce postharvest losses of avocado fruit by determining internal fruit quality of individual fruit quickly from flesh or skin images. Full article

(This article belongs to the Special Issue Digital Farming, Food, and Environment—a Response to Farm Biosecurity and Food Safety Concerns)

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